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Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis
Agents of any metaheuristic algorithms are moving in two modes, namely
exploration and exploitation. Obtaining robust results in any algorithm is
strongly dependent on how to balance between these two modes. Whale
optimization algorithm as a robust and well recognized metaheuristic algorithm
in the literature, has proposed a novel scheme to achieve this balance. It has
also shown superior results on a wide range of applications. Moreover, in the
previous chapter, an equitable and fair performance evaluation of the algorithm
was provided. However, to this point, only comparison of the final results is
considered, which does not explain how these results are obtained. Therefore,
this chapter attempts to empirically analyze the WOA algorithm in terms of the
local and global search capabilities i.e. the ratio of exploration and
exploitation phases. To achieve this objective, the dimension-wise diversity
measurement is employed, which, at various stages of the optimization process,
statistically evaluates the population's convergence and diversity.Comment: 11 page